35 lines
1.4 KiB
R
35 lines
1.4 KiB
R
library(tidyverse)
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library(forecast)
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source("Scripts/Functions.r")
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#source("Scripts/Load_Wyoming_Web_Data.r")
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DF <- FRED_GET('WYLINC3POP','LN_POP') %>% select(-YEAR)
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TS <- 1000*ts(DF,start=c(1970),end=c(2024),frequency=1)
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BC <- BoxCox.lambda(TS)
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MODEL <- auto.arima(TS, lambda = BC)
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forecast(MODEL,h=20)
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plot(forecast(MODEL,h=35),main="Lincoln County Population Forecast")
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####Employment to pop ratio
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EMP <- FRED_GET('LAUCN560230000000005','EMP') %>% inner_join(FRED_GET('WYLINC3POP','LN_POP')) %>% mutate(LN_POP=1000*LN_POP)
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EMP <- EMP %>% mutate(RATIO=LN_POP/EMP)
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ggplot(aes(x=YEAR,y=RATIO),data=EMP)+geom_line()
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AVG_POP_RATIO <- mean(EMP$RATIO)
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SD_POP_RATIO <- sd(EMP$RATIO)
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#####Plan and ideas
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#1) Review IMPLAN for industry multipliers
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#2) Review IMPLAN for employment to population multipliers (imparted)
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#3) Find a list of all planned new projects
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#4) Use the IMPLAN multipliers for each sector to estimate total change
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#5) Develop survey to estimate likelihood of new projects
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#6) Compare to the ARMA percentile
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#7) Adjust the ARMA up assuming some of these outputs are known.
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#8) Occupancy rate from IMPLAN as a housing cap when projecting
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#9) Model housing construciton rate (Maybe)
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#10) Employment rate by age in IMPLAN
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####Other ideas, develop larger plan? Maybe look at decline in other industries as a proportion of employment
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###Seperate out Kemmer and Diamondville? http://eadiv.state.wy.us/pop/wyc&sc40.htm
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